The task of maintaining product quality in a chemical process is one of the basic tasks in the chemical and petroleum industries, and also has been the subject of continuing, intense scrutiny for many years now. Advances have been many and profound, beginning with occasional sampling of one or more products and attendant time-consuming analysis, comparison of the analytical data for the product with data for some "standard," and subsequent adjustment of the process according to empirical, often variable rules as an attempt to compensate for observed product changes. This approach had numerous failings. Sampling times were erratic and analyses often were time consuming, so that the analytical data were relevant to a process state far removed in time from that required. Relations between product differences and process variables were poorly understood and often were of limited corrective value, and different operators sometimes used quite different rules, so that product quality might vary depending upon the operator shifts. Interpretation of analytical results also was subjective, which together with the subjectivity of the effects of process variables on product could certainly lead to non-uniform process control not only at a particular site, but also among different reactors at a site, and certainly could lead to non-uniformity across different sites. Moreover, the samples removed from the process stream could undergo changes as a result of the change in environment (e.g., temperature, pressure, etc.) relative to the process stream.
In time, process control assumed increasing sophistication. In particular, analytical results were incorporated into a feedback loop, often at several points, so that variables could be changed at different points in the process. Analyses also could be taken of streams at different process points, and used either to control a process variable at a specified process point or to control several process variables concurrently, or analyses could be combined to control the variable at several process points. Analytical methods became increasingly sophisticated and, at least in simple cases, were effected in real time, so that the differential between times of obtaining raw data, of reducing the raw data to operationally meaningful data, and of using the latter for process control became more nearly contemporaneous. Finally, the digital revolution and customized or customizable microprocessors permitted controls to be effected extremely rapidly, to be applied to an extended number of process variables, and not only removed subjectivity but included the possibility of "learning on the fly" as the microprocessor and/or associated software gained "experience" in the control process, as, for example, using methods incorporating "artificial intelligence" into the control process. Note, however, that this latter advance was possible only when the necessary analytical data were available on a real-time basis.
This application focuses on spectroscopy as an analytical technique capable of giving truly real-time compositional data, albeit in a complex and non-intuitive form, and develops a particular means to use spectroscopic data in a control process. Emphasis is on spectroscopy because: 1) spectroscopic measurements can be performed continuously in situ and on-line, therefore data are real-time; 2) spectroscopic techniques are widely available; 3) unique and useful compositional information of any particular product is usually available from several regions of the electromagnetic spectrum; and 4) spectral data are amenable to some powerful statistical techniques to afford information especially valuable in control schemes. The following discussion will be couched in terms of near infrared spectroscopy (NIR), but we need to emphasize that our invention is applicable to optical spectroscopy generally, e.g., in the infrared, far infrared, ultraviolet, and visible regions of the electromagnetic spectrum and obtained using absorbance, fluorescence, emission, or Raman measurements. Consequently our use of NIR in the subsequent exposition is solely for clarity and simplicity of presentation, but is otherwise nonexclusive. In addition, the development of other real-time data sampling techniques in the near future will permit our approach to process control to be used equally well with analytical data from nuclear magnetic resonance spectroscopy, mass spectrometry, and fast gas chromatography.